understanding mapreduce

Prerequisite Preparation: 1.hadoop installation is operating normally. Hadoop installation Configuration Please refer to: Ubuntu under Hadoop 1.2.1 Configuration installation 2. The integrated development environment is normal. Integrated development environment Configuration Please refer to: Ubuntu building Hadoop Source Reading environment MapReduce Programming Examples: MapReduce Programming Example (i)

Map/reduce in MongoDB makes some compound queries, because MongoDB does not support group by queries, and MapReduce is similar to SQL's group by, so it can be thought that MapReduce is the MongoDB version of group B Y The command is as follows: Db.runcommand ({mapreduce:, map:, reduce: [, query:] [ , sort:] [, limit:] [, Out:] [, Keeptemp:] [, Finalize:] [,

The Apache Crunch (incubator project) is a Java library based on Google's Flumejava library, which is used to create MapReduce pipelining. Similar to other high-level tools used to create mapreduce jobs, such as Apache Hive, Apache Pig, and cascading, Crunch provides a pattern library for common tasks such as connecting data, performing aggregations, and sorting records. Unlike other tools, crunch does not

1. MongoDB MapReduce is equivalent to Mysql's groupby, so it is easy to use MapReduce for parallel statistics on MongoDB. MapReduce is used to implement two functions: Map function and Reduce function. Map function calls emit (key, value), traverses all records in the collection, and passes the key and value to the Reduce function. 1. MongoDB

First generation of Hadoop composition and structureThe first generation of Hadoop consists of the distributed storage System HDFS and the distributed computing framework MapReduce, in which HDFs consists of a namenode and multiple datanode, MapReduce consists of a jobtracker and multiple tasktracker, which corresponds to Hadoop 1.x and 0.21.x,0.22.x.1. MapReduce

Hadoop's support for compressed files Hadoop supports transparent identification of compression formats, and execution of our mapreduce tasks is transparent. hadoop can automatically decompress the compressed files for us without worrying about them. If the compressed file has an extension (such as lzo, GZ, and Bzip2) of the corresponding compression format, hadoop selects the decoder to decompress the file based on the extension. Hadoop support

Http://cloud.csdn.net/a/20110224/292508.html The Yahoo! Developer Blog recently sent an article about the Hadoop refactoring program. Because they found that when the cluster reaches 4000 machines, Hadoop suffers from an extensibility bottleneck and is now ready to start refactoring Hadoop. the bottleneck faced by MapReduce The trend observed from cluster size and workload is that MapReduce's jobtracker needs to be overhauled to address its scalabili

It is believed that every programmer will ask himself two questions "How do I accomplish this task" and "How can I get the program to run faster" when programming. Similarly, multiple optimizations of the MapReduce computational model are also designed to better answer these two questions.The optimization of the MapReduce computational model involves all aspects of the content, but the main focus is on two

Transferred from: http://blog.csdn.net/jaytalent?viewmode=contentsMapReduce scheduling and execution Principles series articlesFirst, the MapReduce scheduling and execution principle of the work submittedSecond, the MapReduce scheduling and execution principle of job initializationThird, the task scheduling of MapReduce dispatching and executing principleIv. task

In mapreduce, our custom Mapper and reducer programs may encounter errors and exits after execution. In mapreduce, jobtracker tracks the execution of tasks throughout the process, mapreduce also defines a set of processing methods for erroneous tasks.The first thing to clarify is how mapreduce judges the task failure.

MongoDB MapReduce MapReduce is a computational model that simply executes a large amount of work (data) decomposition (MAP) and then merges the results into the final result (REDUCE). The advantage of this is that after the task is decomposed, it can be computed in parallel by a large number of machines, reducing the time of the entire operation. The above is the theoretical part of

Introduction MapReduce is a programming framework for distributed computing programs and a core framework for users to develop "Hadoop-based data analysis applications";The MapReduce core function is to integrate user-written business logic code and its own default components into a complete distributed computing program, concurrently running on a Hadoop cluster; MAPRE